Big Data in mechanical research: Potentials, applications and challenges

YANG Qiang; MENG Songhe; ZHONG Zheng; XIE Weihua; GUO Zaoyang; JIN Hua; ZHANG Xinghong

doi:10.6052/1000-0992-19-002

Volume 50 Issue 1

Oct. 2020

Turn off MathJax

Article Contents

Article Navigation > Advances in Mechanics > 2020 > 50(1): 202011

YANG Qiang, MENG Songhe, ZHONG Zheng, XIE Weihua, GUO Zaoyang, JIN Hua, ZHANG Xinghong. Big Data in mechanical research: Potentials, applications and challenges[J]. Advances in Mechanics, 2020, 50(1): 202011. doi: 10.6052/1000-0992-19-002

Citation:

YANG Qiang, MENG Songhe, ZHONG Zheng, XIE Weihua, GUO Zaoyang, JIN Hua, ZHANG Xinghong. Big Data in mechanical research: Potentials, applications and challenges[J]. Advances in Mechanics, 2020, 50(1): 202011. doi: 10.6052/1000-0992-19-002

Citation:

PDF( 9177 KB)

Big Data in mechanical research: Potentials, applications and challenges

doi: 10.6052/1000-0992-19-002

¹ Harbin Institute of Technology, National Key Laboratory of Science and Technology for National Defense on Advanced Composites in Special Environments, Harbin 150001, China
² Harbin Institute of Technology, Postdoctoral Research Center for Material Science and Engineering, Harbin 150001, China
³ Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China

More Information

Corresponding author: MENG Songhe
Received Date: 2018-09-05
Publish Date: 2020-10-08

Abstract

Abstract

Big Data has developed rapidly and made remarkable achievements. The unique thinking and methodology of Big Data provide a new paradigm for scientific research. High spatial-temporal resolution and multiple-parameter synchronous observation methods are offering opportunities for Big Data driven mechanics. Applications of Big Data or machine intelligence methods in mechanical researches have grown rapidly. This review is focused on the impact of Big Data method and its way of thinking on mechanical research and the corresponding challenges. First, the connotation and research situation of Big Data in three aspects, i.e. Big Data itself, Big Data science and Big Data technologies are discussed, and Big Data development plans of governments and organizations are summarized. Comparative analysis of the mechanical methodology and the Big Data methodology were carried out, with focuses on paradigm differences in utilization of data. Instead of partial differential equations used by mechanics, data driven models are used for mathematical descriptions of the underlying physical problem in the Big Data paradigm. The latter shows advantages in simulations and predictions of complex systems. Latest researches in material performance prediction, constitutive modeling, turbulence modeling, structural health monitoring, and experimental mechanics using Big Data, as well as Big Data driven new paradigm of modeling and simulation including Dynamic Data Driven Application System and Digital Twin are reviewed. Three kinds of Big Data driven mechanical researches are summarized, i.e. data driven improvement of existing methods, data mining for hidden and intrinsic physical laws, and data based new methodologies and theories for mechanics. A mechanics-centric approach employing both the prior knowledge of mechanics and advantages of Big Data methodology is recommended for a vision of breakthroughs along with new subject directions in mechanics.
- mechanics,
- Big Data,
- data science,
- complexity,
- uncertainty

FullText(HTML)

References(181)

References

[1]	白坤朝, 詹世革, 张攀峰, 谭宗颖, 孟庆国 . 2019. 力学十年: 现状与展望. 力学进展, 49:599-621 (Bai K C, Zhan S G, Zhang P F . 2019. Mechanics 2006-2015: Situation and prospect. Advances in Mechanics, 49: 599-621).
[2]	北京世冠金洋科技发展有限公司. 2018. 自主创新助力国产化进程—世冠科技GCAir4.0正式发布. http://www.globalcrown.com.cn/news/news_dit.html?id=175.[2018-10-19]/[2019-3-11].
[3]	朝乐门, 卢小宾 . 2017. 数据科学及其对信息科学的影响. 情报学报, 36:761-771 (Chao L M, Lu X B . 2017. Data science and its impact on information science. Journal of the China Society for Scientific Information, 36: 761-771).
[4]	戴潘 . 2016. 基于大数据的科学研究范式的哲学研究. 哲学动态, 9:105-109 (Dai P . 2016. Philosophical research on the paradigm of scientific research based on Big Data. Translation of Philosophy, 9: 105-109).
[5]	郭华东, 王力哲, 陈方, 梁栋 . 2014. 科学大数据与数字地球. 科学通报, 59:1047-1054 (Guo H D, Wang L Z, Chen F, Liang D . 2014. Science Big Data and digital earth. Science Bulletin, 59: 1047-1054).
[6]	郭华东 . 2014. 大数据大科学大发现——大数据与科学发现国际研讨会综述. 中国科学院院刊, 4:500-506 (Guo H D . 2014. Big Data big science big discovery—summary of international symposium on Big Data and scientific discovery. Proceedings of the Chinese Academy of Sciences, 4: 500-506).
[7]	黄欣荣 . 2015. 大数据哲学研究的背景、现状与路径. 哲学动态, 7:96-102 (Huang X R . 2015. The background, current situation and path of Big Data philosophy research. Philosophy Dynamics, 7: 96-102).
[8]	霍施宇 . 2014. 热防护结构缝隙热响应分析与设计. [硕士论文]. 哈尔滨: 哈尔滨工业大学 (Huo S Y . 2014. Thermal response analysis and design of thermal protection structure gap. [Master Thesis]. Harbin: Harbin Institute of Technology).
[9]	蓝楠 . 2016. GE力推以结果为导向的大数据分析. 航空维修与工程, 5:43-43 (Lan N . 2016. GE pushes results-oriented Big Data analysis. Aviation Maintenance and Engineering, 5: 43-43).
[10]	李宏男, 高东伟, 伊廷华 . 2008. 土木工程结构健康监测系统的研究状况与进展. 力学进展, 38: 151-166. (Li H N, Gao D W, Yi T H. 2008. Research status and progress of civil engineering structural health monitoring system. Advances in Mechanics, 38: 151-166).
[11]	李惠, 鲍跃全, 李顺龙, 张东昱 . 2015. 结构健康监测数据科学与工程. 工程力学, 32:1-7 (Li H, Bao Y Q, Li S L, Zhang D Y . 2015. Structural health monitoring data science and engineering. Engineering Mechanics, 32: 1-7).
[12]	刘叶婷, 贾童舒 . 2013. 他国如何与大数据"共舞". 信息化建设, 1:16-17 (Liu Y T, Jia T S . 2013. How does his country dance with Big Data. Information Construction, 1: 16-17).
[13]	刘智慧, 张泉灵 . 2014. 大数据技术研究综述. 浙江大学学报(工学版), 48:957-972 (Liu Z H, Zhang Q L . 2014. Summary of Big Data technology research. Journal of Zhejiang University: Engineering Edition, 48: 957-972).
[14]	迈尔·舍恩伯格, 肯尼斯·库克耶 . 2013. 大数据时代: 生活、工作与思维的大变革. 浙江: 浙江人民出版社出版 (Meyer Schonberg, Kenneth Cookeer. 2013. The Era of Big Data: A Major Revolution in Life, Work and Thinking. Zhejiang: Zhejiang People's Publishing House).
[15]	孟小峰, 慈祥 . 2013. 大数据管理: 概念、技术与挑战. 计算机研究与发展, 50:146-169 (Meng X F, Ci X . 2013. Big Data management: Concepts, technologies and challenges. Computer Research and Development, 50: 146-169).
[16]	PTC公司. 2016. PTC 推出最新版本 ThingWorx物联网平台. 智能制造, 8:6-6 ( PTC Corporation. 2016. PTC launches the latest version of ThingWorx IoT platform. Intelligent Manufacturing, 8: 6-6).
[17]	孙松林, 陈娜 . 2016. 大数据助推人工智能. 邮电设计技术, 8:1-5 (Sun S L, Chen N . 2016. Big Data boosts artificial intelligence. Post and TE Design Technology, 8: 1-5).
[18]	陶飞, 张萌, 程江峰 . 2017. 数字孪生车间——一种未来车间运行新模式. 计算机集成制造系统, 23:1-9 (Tao F, Zhang M, Cheng J F . 2017. Digital hygiene workshop—a new mode of future workshop operation. Computer Integrated Manufacturing System, 23: 1-9).
[19]	涂成枫, 刘泽佳, 张舸, 周立成, 陈溢涛, 程楠, 辜家伟, 董守斌, 邓志华, 王勇 , 等. 2017. 面向桥梁长期健康监测的大数据处理技术及应用. 实验力学, 32:652-663 (Tu C F, Liu Z J, Zhang G, Zhou L C, Chen Y T, Cheng N, Gu J W, Dong S B, Deng Z H, Wang Y , et al. 2017. Big Data processing technology and application for long-term health monitoring of bridges. Experimental Mechanics, 32: 652-663).
[20]	王鹏, 孙升, 张庆, 张统一 . 2018. 力学信息学简介. 自然杂志, 40:313-322 (Wang P, Sun S, Zhang Q, Zhang T Y . 2018. Introduction to mechanics informatics. Nature Journal, 40: 313-322).
[21]	魏红江, 李彬, 祝慧琳 . 2016. 制定我国大数据战略与开放数据战略: 日本的经验与启示. 东北亚学刊, 6:32-39 (Wei H J, Li B, Zhu H L . 2016. Formulating China's Big Data strategy and open data strategy: Japan's experience and enlightenment. Northeast Asia Journal, 6: 32-39).
[22]	吴甘沙 . 2015 a. 大数据技术发展的十个前沿方向(上). 大数据, 1:100-111 (Wu G S . 2015a. Ten frontier directions of Big Data technology development (I). Big Data, 1:100-111).
[23]	吴甘沙 . 2015 b. 大数据技术发展的十个前沿方向(中). 大数据, 1:113-123 (Wu G S . 2015b. Ten frontier directions of Big Data technology development (II). Big Data, 1:113-123).
[24]	吴甘沙 . 2015 c. 大数据技术发展的十个前沿方向(下). 大数据, 1:109-117 (Wu G S . 2015c. Ten frontier directions of Big Data technology development (III). Big Data, 1:109-117).
[25]	吴军 . 2016. 智能时代: 大数据与智能革命重新定义未来. 北京: 中信出版社 (Wu J. 2016. Smart Age: Big Data and Intelligent Revolution Redefine the Future. Beijing: CITIC Press Corporation).
[26]	吴思炜, 刘振宇, 周晓光 . 2016. 基于大数据的力学性能预测与工艺参数筛选. 钢铁研究学报, 28:1-4 (Wu S W, Liu Z Y, Zhou X G . 2016. Prediction of mechanical properties based on Big Data and screening of process parameters. Journal of Iron and Steel Research, 28: 1-4).
[27]	杨炳忻 . 2015. 香山科学会议第506~509、S22、510次学术讨论会简述. 中国基础科学, 3:40-45 (Yang B X . 2015. Xiangshan science conference, 506-509, S22, 510 academic seminars. China Basic Science 3:40-45).
[28]	杨强 . 2019. 陶瓷基复合材料损伤行为及其结构响应的不确定性量化方法. [博士论文]. 哈尔滨: 哈尔滨工业大学 (Yang Q . 2019. Uncertainty quantification method for damage behavior and structural responses of ceramic matrix composites. [PhD Thesis]. Harbin: Harbin Institute of Technology).
[29]	张锋军 . 2014. 大数据技术研究综述. 通信技术, 47:1240-1248 (Zhang F J . 2014. Overview of Big Data technology research. Communication Technology, 47: 1240-1248).
[30]	张晓强, 杨君游, 曾国屏 . 2014. 大数据方法: 科学方法的变革和哲学思考. 哲学动态, 8:83-91 (Zhang X Q, Yang J Y, Zeng G P . 2014. Big Data approach: Transformation and philosophical thinking of scientific methods. Philosophical Dynamics, 8: 83-91).
[31]	中国信息化刊. 2017. 实施国家大数据战略加快建设数字中国. 中国信息化, 12:15 ( China Information Technology. . 2017. Implementing national Big Data strategy to accelerate the construction of digital China. China Informatization, 12:15).
[32]	庄存波, 刘检华, 熊辉, 丁晓宇, 刘少丽, 瓮刚 . 2017. 产品数字孪生体的内涵、体系结构及其发展趋势. 计算机集成制造系统, 23:753-768 (Zhuang C B, Liu J H, Xiong H, Ding X Y, Liu S L, Wen G . 2017. The connotation, architecture and development trend of product digital hygiene. Computer Integrated Manufacturing System, 23: 753-768).
[33]	Ackoff R L. 1989. From data to wisdom. Journal of Applied Systems Analysis, 16:3-9.
[34]	Agrawal A, Choudhary A. 2016. Perspective: Materials informatics and Big Data: Realization of the "fourth paradigm" of science in materials science. APL Materials, 4: 053208-1-053208-10.
[35]	Al-Jarrah O Y, Yoo P D, Muhaidat S, Karagiannidis G K, Taha K. 2015. Efficient machine learning for Big Data: A review. Big Data Research, 2:87-93.
[36]	Allaire D, Kordonowy D, Lecerf M, Mainini L, Willcox K. 2014. Multifidelity DDDAS methods with application to a self-aware aerospace vehicle. Procedia Computer Science, 29:1182-1192.
[37]	Ambur M Y, Yagle J J, Reith W, McLarney E. 2016. Big Data analytics and machine intelligence capability development at NASA Langley Research Center: Strategy, roadmap, and progress// NASA Technical Report, 2016, No. NASA/TM-2016-219361.
[38]	Ayensa J J, Doweidar M H, Sanz-Herrera J A, Doblare M. 2018. A new reliability-based data-driven approach for noisy experimental data with physical constraints. Computer Methods in Applied Mechanics and Engineering, 328:752-774.
[39]	Banerjee A, Bandyopadhyay T, Acharya P. 2013. Data analytics: Hyped up aspirations or true potential? Vikalpa, 38:1-12.
[40]	Batra S. 2014. Big Data analytics and its reflections on DIKW hierarchy. Review of Management, 4:5.
[41]	Belianinov A, Vasudevan R, Strelcov E, Steed C, Yang S M, Tselev A, Jesse S, Biegalski M, Shiman G, Symons C , et al. 2015. Big Data and deep data in scanning and electron microscopies: Deriving functionality from multidimensional data sets. Advanced Structural and Chemical Imaging, 1:6.
[42]	Belle A, Thiagarajan R, Soroushmehr S M, Navidi F, Beard D A, Najarian K. 2015. Big Data analytics in healthcare. BioMed Research International, 54:546-547.
[43]	Bessa M A, Bostanabad R, Liu Z, Hu A, Apley D W, Brinson C, Chen W, Liu W K. 2017. A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality. Computer Methods in Applied Mechanics and Engineering, 320:633-667.
[44]	Bryant R, Katz R, Lazowska E. 2008. Big-data computing: Creating revolutionary breakthroughs in commerce. Science and Society, 8:1-15.
[45]	Brynjolfsson E, Hitt L M, Kim H H. 2011. Strength in numbers: How does data-driven decision making affect firm performance? https://ssrn.com/abstract=1819486. [2011-4-22]/[2019-03-11].
[46]	Burrows B, Isaac B, Allaire D L. 2016. A dynamic data-driven approach to multiple task capability estimation for self-aware aerospace vehicles// 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2016, Washington, D.C.USA.
[47]	Cai G. 2017. Big Data analytics in structural health monitoring. [PhD Thesis]. Nashville, USA: Vanderbilt University.
[48]	Cayton L. 2005. Algorithms for manifold learning. Univ. of California at San Diego Tech, 12:1-17.
[49]	Cearley D, Burke B, Searle S, Walker M. 2017. Top 10 strategic technology trends for 2018. https://www.gart-ner.com/doc/3811368?srcId=1-8622966154. [2017-10-03]/[2019-3-11].
[50]	Cohen P R. 2015. DARPA's big mechanism program. Physical Biology, 12:045008.
[51]	Crespo J, Latorre M, Montáns F J. 2017. WYPIWYG hyperelasticity for isotropic, compressible materials. Computational Mechanics, 59:73-92.
[52]	Darema F. 2008. Dynamic data driven applications systems—a transformative paradigm// International Conference on Computational Science. Springer, Berlin, Heidelberg.
[53]	Darema F. 2018. Grids-of-grids and autonomy// 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA.
[54]	De Angelis E, Casciola C M, Piva R. 2002. DNS of wall turbulence: Dilute polymers and self-sustaining mechanisms. Computers & Fluids, 31:495-507.
[55]	Deka G C. 2014. Big Data predictive and prescriptive analytics//Handbook of Research on Cloud Infrastructures for Big Data Analytics. USA: IGI Global Press, 370-391.
[56]	Demchenko Y, Grosso P, De Laat C, Membrey P. 2013. Addressing Big Data issues in scientific data infrastructure// 2013 International Conference on Collaboration Technologies and Systems, San Diego, CA, USA, 48-55.
[57]	Dhar V. 2013. Data science and prediction. Communications of the ACM, 56:64-73.
[58]	Dimiduk D M, Holm E A, Niezgoda S R. 2018. Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integrating Materials and Manufacturing Innovation, 7:157-172.
[59]	Dion B. 2016 2016. Predictive analytics and the digital twin// TERATEC Forum, 2016 France. http://www.teratec.eu/library/pdf/forum/2016/Presentations/A7_01_Forum_TERATEC_2016_DION_ANSYS.pdf.
[60]	Douglas C C. 2008. Dynamic data driven applications systems-DDDAS 2008// International Conference on Computational Science. Springer, Berlin, Heidelberg.
[61]	Duncan G R. 2008. Big Data special. Nature, 455:7209.
[62]	Duraisamy K, Alonso J , DurbinP. 2017. A framework for turbulence modeling using Big Data: Phase II final report. NASA Technical Report, 2017, No. NNX15AN98A.
[63]	Duraisamy K, Iaccarino G, Xiao H. 2019. Turbulence modeling in the age of data. Annual Review of Fluid Mechanics, 51:357-377.
[64]	Ekbia H, Mattioli M, Kouper I, Arave G, Ghazinejad A, Bowman T, Suri V R, Tsou A, Weingart S, Sugimoto C R. 2015. Big Data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66:1523-1545.
[65]	Eshghinejad A, Nasr Esfahani E, Wang P, Xie S H, Geary T C, Adler S B, Li J Y. 2016. Scanning thermoionic microscopy for probing local electrochemistry at the nanoscale. Journal of Applied Physics, 119:205110.
[66]	Farrar C R, Worden K. 2006. An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365:303-315.
[67]	Farrar C R, Worden K. 2012. Structural Health Monitoring: A Machine Learning Perspective. United Kingdom: John Wiley & Sons.
[68]	Fricke M. 2015. Big Data and its epistemology. Journal of the Association for Information Science and Technology, 66:651-661.
[69]	Gandomi A H, Sajedi S, Kiani B, Huang Q. 2016. Genetic programming for experimental Big Data mining: A case study on concrete creep formulation. Automation in Construction, 70:89-97.
[70]	Gandomi A, Haider M. 2015. Beyond the hype: Big Data concepts, methods, and analytics. International Journal of Information Management, 35:137-144.
[71]	Gantz J, Reinsel D. 2011. Extracting value from chaos. IDC iView, 1142:1-12.
[72]	Genovese Y, Prentice S. 2011. Pattern-based strategy: getting value from Big Data. Gartner Special Report, 2011, No. G00214032. https://www.gartner.com/en/documents/1727419.
[73]	Glaessgen E, Stargel D. 2012. The digital twin paradigm for future NASA and US Air Force vehicles// 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 2012, Honolulu, Hawaii, USA.
[74]	Gonzalez D, Chinesta F, Cueto E. 2019. Thermodynamically consistent data-driven computational mechanics. Continuum Mechanics and Thermodynamics, 31:239-253.
[75]	Green M L, Choi C L, Hattrick-Simpers J R, Joshi A M, Takeuchi I, Barron S C. 2017. Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies. Applied Physics Reviews, 4:011105.
[76]	Grieves M, Vickers J. 2017. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems//Transdisciplinary Perspectives on Complex Systems, Switzerland: Springer Cham.
[77]	Groves P, Kayyali B, Knott D, Kuiken S V. 2013. The "Big Data" revolution in healthcare. McKinsey Quarterly, 28:2.
[78]	Gu J, Zhang L. 2014. Some comments on Big Data and data science. Annals of Data Science, 1:283-291.
[79]	Gulgec N S, Shahidi G S, Matarazzo T J, Pakzad S N. Current challenges with Big Data analytics in structural health monitoring// Structural Health Monitoring & Damage Detection, Switzerland: Springer Cham, 7:79-84.
[80]	Guo S, Yu J, Liu X, Wang C, Jiang Q. 2019. A predicting model for properties of steel using the industrial Big Data based on machine learning. Computational Materials Science, 2019: 95-104.
[81]	Han J. 2017. On the power of Big Data: Mining structures from massive, unstructured text data// IEEE International Conference on Big Data, 2016, Washington, DC, USA.
[82]	Hashem I A T, Yaqoob I, Anuar N B, Mokhtar S, Gani A, Khan S U. 2015. The rise of "Big Data" on cloud computing: Review and open research issues. Information Systems, 47:98-115.
[83]	Hild F, Roux S. 2006. Digital image correlation: From displacement measurement to identification of elastic properties—a review. Strain, 42:69-80.
[84]	Huang B, Li Z, Li J. 2018. An artificial intelligence atomic force microscope enabled by machine learning. Nanoscale, 10:21320-21326.
[85]	Ibanez R, Abisset-Chavanne E, Aguado J V, Gonzalez D, Cueto E, Chinesta F. 2018. A manifold learning approach to data-driven computational elasticity and inelasticity. Archives of Computational Methods in Engineering, 25:47-57.
[86]	Ibanez R, Borzacchiello D, Aguado J V, Abisset-Chavanne E, Cueto E, Ladeveze P, Chinesta F. 2017. Data-driven non-linear elasticity: Constitutive manifold construction and problem discretization. Computational Mechanics, 60:813-826.
[87]	Inmon W H, Imhoff C, Battas G. 1999. Building the Operational Data Store. United Kingdom: John Wiley.
[88]	Javadi A A, Ahangar-Asr A, Johari A, Faramarzi A, Toll D. 2012. Modelling stress-strain and volume change behaviour of unsaturated soils using an evolutionary based data mining technique, an incremental approach. Engineering Applications of Artificial Intelligence, 25:926-933.
[89]	Jifa G, Lingling Z. 2014. Data, DIKW, Big Data and data science. Procedia Computer Science, 31:814-821.
[90]	Jin C. 2017. Real-time damage detection for civil structures using Big Data. [PhD Thesis]. Connecticut: University of Connecticut.
[91]	Kaisler S, Armour F, Espinosa J A, Money W. 2013. Big Data: Issues and challenges moving forward// 46th Hawaii International Conference on System Sciences, 2013, Hawaii, USA.
[92]	Kalidindi S R, De Graef M. 2015. Materials data science: Current status and future outlook. Annual Review of Materials Research, 45:171-193.
[93]	Kambatla K, Kollias G, Kumar V, Grama A. 2014. Trends in Big Data analytics. Journal of Parallel and Distributed Computing, 74:2561-2573.
[94]	Kaplinski O, Koseleva N, Ropaite G. 2016. Big Data in civil engineering: A state-of-the-art survey. Engineering Structures and Technologies, 8:165-175.
[95]	Katal A, Wazid M, Goudar R H. 2013. Big Data: Issues, challenges, tools and good practices// 2013 Sixth International Conference on Contemporary Computing, 2013, Noida, India.
[96]	Kirchdoerfer T, Ortiz M. 2016. Data-driven computational mechanics. Computer Methods in Applied Mechanics and Engineering, 304:81-101.
[97]	Kirchdoerfer T, Ortiz M. 2017. Data driven computing with noisy material data sets. Computer Methods in Applied Mechanics and Engineering, 326:622-641.
[98]	Kirkpatrick R. 2013. Big Data for development. Big Data, 1:3-4.
[99]	Kitchin R. 2014. Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1:1-12.
[100]	Kong C S, Broderick S R, Jones T E, Loyola C, Eberhart M E, Rajan K. 2015. Mining for elastic constants of intermetallics from the charge density landscape. Physica B: Condensed Matter, 458:1-7.
[101]	Landset S, Khoshgoftaar T M, Richter A N, Hasanin T. 2015. A survey of open source tools for machine learning with Big Data in the Hadoop ecosystem. Journal of Big Data, 2:24.
[102]	Lecerf M, Allaire D, Willcox K. 2015 Methodology for dynamic data-driven online flight capability estimation. AIAA Journal, 53:3073-3087.
[103]	Leplay P, Rethore J, Meille S, Baietto M C. 2012. Identification of asymmetric constitutive laws at high temperature based on digital image correlation. Journal of the European Ceramic Society, 32:3949-3958.
[104]	Li H, Ou J. 2016. The state of the art in structural health monitoring of cable-stayed bridges. Journal of Civil Structural Health Monitoring, 6:43-67.
[105]	Li X, Liu Z, Cui S, Luo C, Li C, Zhuang Z. 2019. Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning. Computer Methods in Applied Mechanics and Engineering, 347:735-753.
[106]	Liang Y, Wu D, Huston D, Liu G, Li Y, Gao C, Ma Z J. 2018. Civil infrastructure serviceability evaluation based on Big Data// Guide to Big Data Applications. Switzerland: Springer Cham.
[107]	Liang Y, Wu D, Liu G, Li Y, Gao C, Ma Z J, Wu W. 2016. Big Data-enabled multiscale serviceability analysis for aging bridges. Digital Communications and Networks, 2:97-107.
[108]	Lin Y, Nie Z, Ma H. 2017. Structural damage detection with automatic feature—extraction through deep learning. Computer Aided Civil and Infrastructure Engineering, 32:1025-1046.
[109]	Ling J, Kurzawski A, Templeton J. 2016. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. Journal of Fluid Mechanics, 807:155-166.
[110]	Mainini L, Willcox K. 2015. Surrogate modeling approach to support real-time structural assessment and decision making. AIAA Journal, 53:1612-1626.
[111]	Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers A H. 2011. Big Data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute Report, 2011, https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-\for-innovation.
[112]	Mauro A D, Greco M, Grimaldi M. 2016. A formal definition of Big Data based on its essential features. Library Review, 65:122-135.
[113]	Mazzocchi F. 2015. Could Big Data be the end of theory in science? A few remarks on the epistemology of data driven science. EMBO Reports, 16:1250-1255.
[114]	McDowell D L, LeSar R A. 2016. The need for microstructure informatics in process-structure-property relations. MRS Bulletin, 41:587-593.
[115]	Meng S, Song L, Xu C, Wang W, Xie W H, Jin H. 2017. Predicting the effective properties of 3D needled carbon/carbon composites by a hierarchical scheme with a fiber-based representative unit cell. Composite Structures, 172:198-209.
[116]	Meng S, Zeng Q, Jin H, Wang L W, Xu C H. 2018. Evaluation of atomic oxygen catalytic coefficient of ZrB2-SiC by laser-induced fluorescence up to 1473 K. Measurement Science and Technology, 29:075207.
[117]	Min C, Mao S, Liu Y. 2014 b. Big Data: A survey. Mobile Networks & Applications, 19:171-209.
[118]	Min C, Mao S, Yin Z, Leung V. 2014 a. Big Data storage// Big Data Related Technologies, Challenges and Future Prospects. Switzerland: Springer Cham.
[119]	Minano M, Montans F J. 2018. WYPiWYG damage mechanics for soft materials: A data-driven approach. Archives of Computational Methods in Engineering, 25:165-193.
[120]	Morrison J H, Ambur M Y, Bauer S X. 2016. Comprehensive digital transformation NASA Langley Research Center. NASA Technical Report, 2016, No. NF1676L-25664.
[121]	Najafabadi M M, Villanustre F, Khoshgoftaar T M, Seliya N, Wald R, Muharemagic E. 2015. Deep learning applications and challenges in Big Data analytics. Journal of Big Data, 2:1.
[122]	Neggers J, Allix O, Hild F, Roux S. 2018. Big Data in experimental mechanics and model order reduction: Today's challenges and tomorrow's opportunities. Archives of Computational Methods in Engineering, 25:143-164.
[123]	Nguyen L T K, Keip M A. 2018. A data-driven approach to nonlinear elasticity. Computers & Structures, 194:97-115.
[124]	O'Leary D E. 2013. Artificial intelligence and Big Data. IEEE Intelligent Systems, 28:96-99.
[125]	Ozkose H, Ari E S, Gencer C. 2015. Yesterday, today and tomorrow of Big Data. Procedia-Social and Behavioral Sciences, 195:1042-1050.
[126]	Prudencio E E, Bauman P T, Faghihi D, Ravi-Chandar K, Oden J T. 2015. A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection. International Journal for Numerical Methods in Engineering, 102:379-403.
[127]	Qi G, Wayne S F. 2014. A framework of data-enabled science for evaluation of material damage based on acoustic emission. Journal of Nondestructive Evaluation, 33:597-615.
[128]	Qiu J, Wu Q, Ding G, Feng S. 2016. A survey of machine learning for Big Data processing. EURASIP Journal on Advances in Signal Processing, 2016: 67.
[129]	Raj P. 2018. A detailed analysis of NoSQL and NewSQL databases for Big Data analytics and distributed computing. Advances in Computers, 109:1-48.
[130]	Rajan K. 2015. Materials informatics: The materials "gene" and Big Data. Annual Review of Materials Research, 45:153-169.
[131]	Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. 2017. Machine learning in materials informatics: Recent applications and prospects. NPJ Computational Materials, 3:54.
[132]	Reddy K K, Venugopalan V, Giering M J. 2016. Applying deep learning for prognostic health monitoring of aerospace and building systems//1st ACM SIGKDD Workshop on ML for PHM, 2016, USA.
[133]	Rowley J. 2007. The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science, 33:163-180.
[134]	Rudy S H, Brunton S L, Proctor J L, Kutz J N. 2017. Data-driven discovery of partial differential equations. Science Advances, 3:e1602614.
[135]	Russom P. 2011. Big Data analytics. TDWI best practices report, 2011. https://tdwi.org/research/2011/09/~/media/TDWI/TDWI/Research/BPR/2011/TDWI_BPReport_Q411_Big_Data_Analytics_Web/TDWI_\BPReport_Q411_Big%20Data_ExecSummary.ashx.
[136]	Rzhetsky A. 2016. The big mechanism program: Changing how science is done// 18th International Conference on Data Analytics and Management in Data Intensive Domains, 2016, Ershovo, Russia.
[137]	Sanchez S M. 2014. Simulation experiments: Better data, not just Big Data// 2014 Winter Simulation Conference, 2014, Savanah USA.
[138]	Schleich B, Anwer N, Mathieu L, Wartzack S. 2017. Shaping the digital twin for design and production engineering. CIRP Annals, 66:141-144.
[139]	Schroeck M, Shockley R, Smart J, Romero-Morales D, Tufano P. 2012. Analytics: The real-world use of Big Data. IBM Global Business Services Report, 2012. .
[140]	Science staff. 2011. Dealing with data. Science, 331:639-806.
[141]	Shafto M, Conroy M, Doyle R , et al. 2012. Modeling, simulation, information technology & processing roadmap. NASA Technical Report, 2012, https://www.nasa.gov/pdf/501321main_TA11-MSITP-DRAFT-Nov2010-A1.pdf.
[142]	Shakoor M, Kafka O L, Yu C, Liu W K. 2018. Data science for finite strain mechanical science of ductile materials. Computational Mechanics, 13:1-13.
[143]	Singh A P, Medida S, Duraisamy K. 2017. Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils. AIAA Journal, 2215-2227.
[144]	Singh D, Reddy C K. 2015. A survey on platforms for Big Data analytics. Journal of Big Data, 2:8.
[145]	Sivarajah U, Kamal M M, Irani Z, Weerakkody V. 2017. Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70:263-286.
[146]	Spalart P R. 2000. Strategies for turbulence modelling and simulations. International Journal of Heat and Fluid Flow, 21:252-263.
[147]	Speziale C. 1998. Turbulence modeling for time-dependent RANS and VLES: A review. AIAA Journal, 36:173-184.
[148]	Succi S, Coveney P V. 2019. Big Data: The end of the scientific method? Philosophical Transactions of the Royal Society A, 377:20180145.
[149]	Sumpter B G, Vasudevan R K, Potok T, Kalinin S V. 2015. A bridge for accelerating materials by design. NPJ Computational Materials, 1:15008.
[150]	Sun Y, Song H, Jara A J , et al. 2016. Internet of things and Big Data analytics for smart and connected communities. IEEE Access, 4:766-773.
[151]	Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F Y. 2018. Digital twin-driven product design, manufacturing and service with Big Data. The International Journal of Advanced Manufacturing Technology, 94:3563-3576.
[152]	Terzi D S, Terzi R, Sagiroglu S. 2015. A survey on security and privacy issues in Big Data// .
[153]	Tsai C W, Lai C F, Chao H C, Vasilakos A V. 2015. Big Data analytics: A survey. Journal of Big Data, 2:21.
[154]	Tuegel E J, Ingraffea A R, Eason T G, Spottswood S M. 2011. Reengineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engineering, 2011: 154798.
[155]	Uhlemann T H J, Lehmann C, Steinhilper R. 2017. The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia CIRP, 61:335-340.
[156]	Ur Rehman M H, Liew C S, Abbas A, Jayaraman P P, Wah T Y, Khan S U. 2016. Big Data reduction methods: A survey. Data Science and Engineering, 1:265-284.
[157]	US Government. 2012. Big Data research and development initiative. https://obamawhitehouse.archives.gov/blog/2012/03/29/big-data-big-deal, [2012-03-29]/[2019-3-11].
[158]	Ushizima D M, Bale H A, Bethel E W, Ercius P, Helms B A, Krishnan H, Grinberg L T, Haranczyk M, Macdowell A A, Odziomek K. 2016. Ideal: Images across domains, experiments, algorithms and learning. JOM, 68:2963-2972.
[159]	Ushizima D, Perciano T, Krishnan H, Loring B, Bale H, Parkinson D, Sethian J. 2014. Structure recognition from high resolution images of ceramic composites// 2014 IEEE International Conference on Big Data. 2014, Washington, DC, USA.
[160]	Viceconti M, Hunter P J, Hose R D. 2015. Big Data, big knowledge: Big Data for personalized healthcare. IEEE J. Biomedical and Health Informatics, 19:1209-1215.
[161]	Wamba S F, Akter S, Edwards A, Chopin G, Gnanzou D. 2015. How "Big Data" can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165:234-246.
[162]	Wang J P, Zhang W S, Shi Y K, Duan S, Liu J. 2018. Industrial Big Data analytics: Challenges, methodologies, and applications. arXiv preprint. .
[163]	Wang J X, Wu J L, Xiao H. 2017. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data. Physical Review Fluids, 2:034603.
[164]	Wang K, Sun W C. 2018. Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning. arXiv preprint. https://arxiv.org/abs/1810.10535.
[165]	Wang W, Xu C, Jin H, Meng S H, Zhang Y, Xie W H. 2017. Measurement of high temperature full-field strain up to 2000 C using digital image correlation. Measurement Science and Technology, 28:035007.
[166]	White A A. 2013. Big Data are shaping the future of materials science. MRS Bulletin, 38:594-595.
[167]	Witherden F D, Jameson A. 2017. Future directions in computational fluid dynamics// 23rd AIAA Computational Fluid Dynamics Conference, 2017, Denver, Colorado, USA.
[168]	Wu J L, Sun R, Laizet S, Xiao H. 2019. Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling. Computer Methods in Applied Mechanics and Engineering, 346:707-726.
[169]	Wu X, Zhao J, Tong Y. 2018. Big Data analysis and scheduling optimization system oriented assembly process for complex equipment. IEEE Access, 6:36479-36486.
[170]	Xu C H, Han X X, Song L Y, Meng S H, Xie W H, Jin H. 2018. A modified anti-symmetric four-point bending method for testing C/C composites under biaxial shear and compression. Experimental Mechanics, 58:515-525.
[171]	Yan D, Yin X S, Lian C, Zhong X, Zhou X, Wu G S. 2015. Using memory in the right way to accelerate Big Data processing. Journal of Computer Science and Technology, 30:30-41.
[172]	Yan J, Meng Y, Lu L, Li L. 2017. Industrial Big Data in an industry 4.0 environment: Challenges, schemes, and applications for predictive maintenance. IEEE Access, 5:23484-23491.
[173]	Yang Q, Han X, Xu C, Xie W H, Meng S H. 2018. Development and validation of an anisotropic damage constitutive model for C/SiC composite. Ceramics International, 44:22880-22889.
[174]	Yang W, Wang H T, Li T F, Qu S. 2019. X-Mechanics—an endless frontier. Science China Physics, Mechanics & Astronomy, 62:14601.
[175]	Yang Z, Yabansu Y C, Al-Bahrani R, Liao W K, Choudhary A N, Kalidindi S R, Agrawal A. 2018. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Computational Materials Science, 151:278-287.
[176]	Yvonnet J, Gonzalez D, He Q C. 2009. Numerically explicit potentials for the homogenization of nonlinear elastic heterogeneous materials. Computer Methods in Applied Mechanics and Engineering, 198:2723-2737.
[177]	Zhang C Y, Chen C L P. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275:314-347.
[178]	Zhang X, Xiang Y. 2017. Combinatorial approaches for high-throughput characterization of mechanical properties. Journal of Materiomics, 3:209-220.
[179]	Zhang Y, Ren J, Liu J, Xu C, Guo H, Liu Y. 2017. A survey on emerging computing paradigms for Big Data. Chinese Journal of Electronics, 26:1-12.
[180]	Zhou L, Pan S, Wang J, Vasilakos A V. 2017. Machine learning on Big Data: Opportunities and challenges. Neurocomputing, 237:350-361.
[181]	Zhou Z H, Chawla N V, Jin Y, Williams G J. 2014. Big Data opportunities and challenges: Discussions from data analytics perspectives. IEEE Computational Intelligence Magazine, 9:62-74.